Efficient Simulation Based Calibration of Automated Driving Functions Based on Sensitivity Based Optimization

Fraikin, Nicolas; Funk, Kilian; Frey, Michael; Gauterin, Frank

doi:10.1109/ojits.2020.3001801

Cited by 5 publications

(2 citation statements)

References 35 publications

(39 reference statements)

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“…Vehicle dynamics models have also been analysed towards their sensitivities, as the quality of the used vehicle dynamics models has an effect on AV simulations [17]. In order to find the optimal calibration of the trajectory planning module of an AV for several different functional scenarios, sensitivity analyses can be used to reduce the complexity of a genetic algorithm based optimisation as shown in Reference [18]. The sensitivity analysis is herein used to restrict the search space of possible calibration parameters by excluding parameters with a sensitivity below a certain threshold from further simulation.…”

Section: Scenario-based Validation Of Avsmentioning

confidence: 99%

Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

et al. 2021

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Assuring the safety of automated vehicles is essential for their timely introduction and acceptance by policymakers and the public. To assess their safe design and robust decision making in response to all possible scenarios, new methods that use a scenario-based testing approach are needed, as testing on public roads in normal traffic would require driving millions of kilometres. We make use of the scenario-based testing approach and propose a method to model simulated scenarios using Gaussian Process based models to predict untested scenario outcomes. This enables us to efficiently determine the performance boundary, where the safe and unsafe scenarios can be evidently distinguished from each other. We present an iterative method that optimises the parameter space of a logical scenario towards the most critical scenarios on this performance boundary. Additionally, we conduct a novel probabilistic sensitivity analysis by efficiently computing several variance-based sensitivity indices using the Gaussian Process models and evaluate the relative importance of the scenario input parameters on the scenario outcome. We critically evaluate and investigate the usefulness of the proposed Gaussian Process based approach as a very efficient surrogate model, which can model the logical scenarios effectively in the presence of uncertainty. The proposed approach is applied on an exemplary logical scenario and shows viability in finding concrete critical scenarios. The reported results, derived from the proposed approach, could pave the way to more efficient testing of automated vehicles and instruct further physical tests on the determined critical scenarios.

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Section: Scenario-based Validation Of Avsmentioning

confidence: 99%

Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

et al. 2021

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“…At the same time, methods for using special calibration objects are also being investigated. [11] Calibration objects that are commonly utilized include mostly 2D and 3D objects, as well as custom objects such as flat checkerboard and spherical surfaces. Zhang and Wang [12] used Inverse Perspective Mapping (IPM) to calibrate several onboard cameras by placing multiple checkerboards on the ground.…”

Section: Introductionmentioning

confidence: 99%

An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV

Yuesheng,

Tao,

Long

et al. 2024

IEEE Open J. Intell. Transp. Syst.

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Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras' extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.

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Electric Vehicle Physical Parameters Identification

Maia

Mendes

Araújo

2022

IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society

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Efficient Simulation Based Calibration of Automated Driving Functions Based on Sensitivity Based Optimization

Cited by 5 publications

References 35 publications

Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV

Electric Vehicle Physical Parameters Identification

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